Volume 14, Issue 1 (1-2025)
JCHR 2025, 14(1): 97-103 |
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Ajmal Dina M, Madidadizadeh F, Arshed A. Tutorial on Methods to Adjust for Confounding Variable in Medical Research. JCHR 2025; 14 (1) :97-103
URL: http://jhr.ssu.ac.ir/article-1-1108-en.html
URL: http://jhr.ssu.ac.ir/article-1-1108-en.html
1- Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
2- Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran ,statlearningeasy@gmail.com
3- Rahbar Medical and Dental College, Lahore Pakistan
2- Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran ,
3- Rahbar Medical and Dental College, Lahore Pakistan
Abstract: (159 Views)
Background: Confounders can distort the actual connection between exposure and outcome, resulting in skewed results. In research, it is essential to account for confounding variables to preserve the validity of causal inferences.
Methods: In this narrative review study, all statistical methods for adjusting confounding variable such as standardization, propensity score, stratification, restriction, statistical model for control, matching, randomization were reviewed.
Results: The five most important methods were reviewed.
Conclusion: Adequate adjustment improves the internal validity of findings and elucidates the relationships among variables, underscoring the importance of a comprehensive analysis of confounding for trustworthy research results.
Methods: In this narrative review study, all statistical methods for adjusting confounding variable such as standardization, propensity score, stratification, restriction, statistical model for control, matching, randomization were reviewed.
Results: The five most important methods were reviewed.
Conclusion: Adequate adjustment improves the internal validity of findings and elucidates the relationships among variables, underscoring the importance of a comprehensive analysis of confounding for trustworthy research results.
Review: Review |
Subject:
Epidemiology
Received: 2024/11/12 | Accepted: 2025/04/30 | Published: 2025/06/9
Received: 2024/11/12 | Accepted: 2025/04/30 | Published: 2025/06/9
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